As the coronavirus spread in Philadelphia this year, travel patterns changed. With this shock to human mobility came another to business activity, as far fewer of us traveled to work, amenities or other pastimes. In order to understand the consequences of these changes, we use mobile phone GPS data to explore how Philadelphians changed their travel patterns before and during the pandemic. This analysis will explore general trends before examining night life—the restaurants and bars that both provide jobs and support vibrant streets—and certain bellwether industries, like those with office work, that doubtless provide a foundation for such night life.
With the goal of understanding the time-space patterns of resident movement in Philadelphia, the following section presents data from SafeGraph, a provider of such records. Note that SafeGraph collects data on a representative sample (10%) of the population across the country, so our indicators are not the true number of visits or journeys, but a slice. The data contain the terms defined in figure 1.1: the number of visitors is the count of devices flowing to a point of interest—be it from a given Census Block Group or total—while a connection is an origin-destination line between a Census Block Group and a point of interest, regardless of its weight. Our concern here is to track the flow of visitors from an origin location to a destination location in order to map flows over time and space.
Figure 1.2 below maps these origin-destination flows as network connections to show the extent to which areas throughout Philadelphia are connected to one another. Changes over time reveal how much the network thins out as the pandemic grows.
Each visit is a mobile device entering into a point of interest; these include parks and museums, restaurats and bars, or offices and hospitals. In figure 1.3 we map the distribution of these venues and businesses for context. We classify each point of interest by its description, which SafeGraph provides. 1 We can see that most businesses cluster in Center City or nearby but no businesses cluster more than restaurants and bars.
This analysis comprises different spatial scales, Citywide, Neighborhood and Point of Interest. We can look globally, across the city, to explore trends throughout; we can also think locally, dividing the city up into cells or neighborhoods to probe variations within the city. Finally, we can look at individual businesses or veneues. Below, we attempt to understand patterns at each scale.
In this section we explore trends and relationships manifest most strongly at the global level, across the city. Best described by this focus on the whole over its part are how certain brands and industries are performing, regardless of location, and how certain variables predict changes to activity and mobility in Philadelphia. We see how visitation is changing across time by tracking visits across brands. Figure 2.1 shows that brands associated with necessities (Target and Walmart) saw comparably less of a decline than others, along with fast food restaurants, which one might expect in a time of constrained budgets. The map shows the locations of brands for context.
| Rankings | Locations |
|---|---|
Figure 2.2, which ranks each brand by the number of visitors it received and animates this change through the pandemic. Dollar stores rise gradually throughout the year, an expected change as residents both need more home goods and need to save money; another important shift is away from non-essential retail towards essential businesses like pharmacies. Starbucks and Wawa occupy top spots for the first several weeks of the year but when the shelter-in-place order sets in, patronage immediately collapses and they are replaced in the ranks essential shops RiteAid and ShopRite.
In figure 2.1 we aggregate by use, grouping by classes like leisure (restaurants and bars) and tourism (museums and theaters). The pandemic had distinct effects on each class, but particularly leisure and other; other includes offices which also explains the steep fall. Interestingly, tourism is recovering while shops and grocers are not, perhaps as many switch to digital commerce.
Changing mobility may influence or exacerbate existing problems in Philadelphia, notably around equity and integration. Philadelphia still shows patterns of concentrated poverty, segregated housing and isolated pockets of prosperty; the pandemic could produce deeper disparities. One risk is that communities of color and low income neighborhoods will not be able to socially distance in the same capacity as affluent communities. The data, however, do not give a clear signal. Below we plot the relationship between outflows—individuals visiting points of interest from a given tract—and key predictors: tract income and the percentage of the tract that is African American. (Tracts allow for better demographic estimates.) The story here is clear for income, as during the critical month of April few poor communities could afford to shelter in place, but hazy for race.
The pandemic appears to have flattened an existing relationship between race and mobility: in early days of the pandemic, communities of color were more likely to receive visitors from the rest of the city, a pattern that held for peak months of spread, but this relationship weakens as more predominately white communities gained visitors in July and August. When we plot the same travel patterns against income, we see that wealthy communites are well below their baseline visits, perhaps because many of the restaurant clusters are in relatively affluent areas. While poor communites are more likely to have recovered to baseline but some of the poorest areas are still lagging behind, more in line with wealthy ones.